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Prolonged Load Carriage During Walking Induces Fatigue and Redistributes Lower Limb Muscle Effort
No full textLoad carriage during walking (e.g., a backpack) is prevalent in everyday tasks as well as in industrial and military settings. During loaded walking, the need to support additional weight and maintain dynamic balance induces immediate changes in joint mechanics and lower body muscle activation patterns. The manifestation of acute changes in workload among lower-limb muscles during prolonged, fatiguing walking bouts remains unclear. Based on evidence that load carriage redistributes the mechanical demands toward distal muscles (e.g., ankle plantar flexors) and away from proximal muscles (e.g. hip extensors), we hypothesize that distal muscles would fatigue at a faster rate than proximal muscles. To test this hypothesis, we recruited 8 young healthy adults and compared the rate of change in the mean power frequency (rMPF), a common biomarker of muscle fatigue, for key ankle and hip muscles over a 30 minute walk during both loaded and unloaded conditions. As expected, load carriage caused an immediate increase in recruitment of active muscle volume that was larger for the ankle than for hip muscles. However, contrary to our hypothesis, we found a larger negative rMPF (i.e. more fatigue) for hip extensors (e.g., biceps femoris = -.29 Hz/min ) than for the ankle extensors (e.g., soleus = .032 Hz/min) during loaded walking (p = 0.0022). This finding suggests the possibility for a motor control strategy that acts to prioritize reliance on proximal muscles during long, highly demanding walking bouts in order to limit fatigue in key distal muscles that are important for efficient propulsive power output.M.S.Bioengineerin
Identifying Meal Preparation Difficulties Faced By People With MCI And Their Care Partners
No full textMild Cognitive Impairment (MCI) is a neurological disease that primarily impacts older adults. MCI makes many aspects of life more challenging, including meal preparation, which is a crucial part of independent living. In my research, I aim to gather primary data for the purpose of creating better Artificial Intelligence technology solutions to empower people with MCI and their care network. To gather this primary data, I run kitchen studies with a pair of participants, one with MCI and one that acts as their care partner. They are given three recipes to create, with ingredients provided, and they divide the work up from there. During a study, we collect video and accelerometer data, as well as detailed after-study surveys. These studies are analyzed and coded for key actions, so that other research groups creating technological solutions for people with MCI can have detailed data of what people with MCI and their care partners actually need to alleviate challenges while performing meal preparation. Some challenges that we’ve identified so far include the fact that people with MCI often struggle with remembering long recipe instructions, the issue that picky eaters may struggle to adequately adapt a recipe, and the problem that signifiers on appliances like fridges to close the door may not always work or be noticed. In the future, I hope to run more primary studies with people with MCI and their care partners, as well as better analyze the already-run studies and collaborate with more research teams to get data for their specific projects.UndergraduateComputer Scienc
Computational Sensemaking for Embodied Co-Creative Artificial Intelligence
No full textThis research lies at the intersection of human-AI interaction, creative collaboration, embodied cognition, and social cognition. Its central aim is to advance the understanding of how AI agents can actively participate in creative processes traditionally dominated by human-human interactions, such as dance and drawing. Grounded in theories of embodiment and intersubjectivity—which prioritize sensory engagement and interaction over abstract cognition—this work explores the complexities of co-creativity and social cognition through the lens of sensemaking within AI systems.
The research investigates how AI systems can engage in co-creative processes by leveraging sensemaking patterns, both descriptively and generatively. It explores how theories of embodiment and sensemaking enhance our understanding of co-creativity, how sensemaking patterns can be analyzed and integrated into co-creative systems, and how design considerations for future co-creative AI systems can be developed. The study employs a mixed-methods approach, combining qualitative and quantitative analyses through empirical studies, interviews, video coding, thematic analysis, surveys, speculative design, and self-reflective exercises.
Key contributions include a new perspective on computational co-creativity that integrates theories from human-computer interaction, embodiment, and social cognition. The dissertation introduces the Observable Creative Sensemaking (OCSM) framework, a method for quantifying sensemaking in embodied creative improvisation. It demonstrates how OCSM can be used descriptively to compare different co-creative interactions and applied as a generative model to guide real-time improvisation. Additionally, the development of two co-creative AI systems—Drawcto, a multi-agent drawing application, and LuminAI, an embodied improvisational dance system—highlights the practical application of these theoretical frameworks and models in real-world AI systems.Ph.D.Digital Medi
Enhancing Human-UGV Teaming in Transportation Tasks Using Wearable Technology
No full textToday, the demands of various labor-intensive roles necessitate workers to manually transport equipment. Unmanned ground vehicles (UGVs) have the potential to offload equipment from human workers and therefore reduce human effort and elevate task performance. In this work, we investigate how wearable technology can enhance the tracking and navigation of human-robot coordination for transportation tasks. This work can be separated into three distinct aims: 1) emulate human strategies to facilitate human-UGV teaming during transportation tasks, 2) analyze the performance and energetics of human-UGV teams in transportation tasks, and 3) leverage wearable technology to enhance the tracking ability of a robot. In this thesis, we develop simulations and physical prototypes to realize our control schemes and our human-robot teaming algorithms. Additionally, we provide insight into our current progress while laying the groundwork for future experiments and development. The key contributions of this work are 1) evaluating the implementation of human following strategies on a human-robot team, 2) facilitating tracking of a target with and without a visual line of sight, and 3) analyzing the human-robot team in terms of efficiency and human energy exertion.Ph.D.Robotic
Automated Root Tracing Using Deep Learning
No full textRoots play a crucial role in plant development by anchoring plants, absorbing nutrients, and maintaining soil structure. Understanding root structures and dynamics is vital for ecological research and assessing soil health. However, tracing roots from photos obtained with Minirhizotron is a time-consuming task, and applying deep learning techniques can facilitate this process. This thesis applies the DeepLabV3+ model with a confidence weighted approach to segment root structures in soil images. The methodology involves classifying images based on root visibility, cropping images to focus on root regions, and training the DeepLabV3+ model, which employs atrous convolutions and an Atrous Spatial Pyramid Pooling (ASPP) module to capture multi-scale contextual information. The confidence method modulates the loss function based on pixel confidence scores to handle ambiguous boundaries and low-resolution images. The confidence function decreases with distance from root boundaries and adapts to varying scales. This method was tested on multiple datasets from natural environments with varying soil types, including Mepibdeath, Ban Harol, Champenoux, and Hesse, which allowed for an assessment of the robustness and generalization ability of the tested models. Evaluated using metrics such as Cohen’s kappa and R2 for surface and length, the results show that the confidence-weighted approach improves segmentation quality by reducing false positives but may miss weakly expressed roots. Future work should focus on enhancing model robustness and improving training data quality to handle complex root structures and environmental noise better.M.S.Computer Scienc
Anticipatory and Reactive Motion Planning for Close-Proximity Human-Robot Interaction
No full textHuman-Robot Collaboration in close-proximity environments requires precise motion planning for an effective partnership between the human and robot. This thesis introduces the motion planning system Real-time Collaboration via Multi-Objective Trajectory Optimization, which possesses both anticipatory and reactive motion planning traits, effectively making it able to plan in real-time. Our real-time motion-planning framework optimizes on a set of both task-based and human-based cost functions. We then formulate a Nonlinear Model-Predictive Control problem and use trajectory optimization to find local optimal solutions for the objective function to optimize the trajectory for small time horizons. This thesis goes in-depth about how the system separates itself from prior work in the field.UndergraduateComputer Scienc
H-BN integration in III-nitride devices for green hydrogen applications
No full textThis thesis explores the initial steps in developing an integrated photoelectrochemical (PEC) cell for green hydrogen production using PEC water splitting. The focus is on integrating InGaN (Indium Gallium Nitride), GaN (Gallium Nitride), and h-BN (hexagonal Boron Nitride), which are promising materials for hydrogen production and storage applications thanks to their unique properties. InGaN, for example, offers a tunable band gap, high chemical stability, and good catalytic activity, making it suitable for hydrogen production. However, its efficiency remains low, and production costs are relatively high. To address these challenges, the thesis investigates the integration of h-BN, a two-dimensional material with a layered structure, into InGaN PEC systems. h-BN can reduce production costs by enabling the reuse of growth substrates when used as a release layer. It can also enhance hydrogen production efficiency by reducing the dislocation density when used as an interfacial layer during growth.
This research includes first, a study of InGaN multiple quantum well photoanodes grown on hBN/sapphire to generate hydrogen via PEC water splitting, revealing the effect of h-BN on surface morphology and charge transfer kinetics. Second, the efficiency of a nanostructured InGaN photoanode in terms of hydrogen production is evaluated through numerical simulations. The most efficient InGaN photoelectrode identified through these studies is selected for a techno-economic analysis to assess the technology viability at a commercial scale. This analysis evaluates the competitiveness of a fully integrated InGaN nanopyramid photoanode with an h-BN proton exchange membrane (PEM). Additionally, the dual functionality of h-BN is highlighted—not only as a PEM to enhance device lifetime under long-term operational conditions but also as a potential system for hydrogen storage at a micro-scale level via the formation of h-BN bubbles under UV light irradiation.Ph.D.Electrical and Computer Engineerin
Differences in Motor Planning Between Intact and Prosthetic Arms
No full textAmputees who have been prescribed prosthetic limbs often abandon them. They do this because learning to use them is challenging, and effective training is difficult. We propose that better training can be offered given the knowledge of how motor planning differs when the end effector is prosthetic as opposed to intact. We studied fMRI data of subjects viewing both intact and prosthetic limbs performing object manipulations to study differences in motor planning. We found that motor planning between the two conditions is functionally different and can be parsed by a machine learning classifier. These findings open the door to further developments in effective prosthetic rehabilitation and general scientific understanding of prosthetic motor planning.UndergraduateNeuroscienc